Publications related to Ab initio study of charged states of H in amorphous SiO2

Machine-learning the electronic density of states: electronic properties without quantum mechanics

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

EPFL2023

Optomechanical response of a strongly interacting Fermi gas

Jean-Philippe Brantut, Timo Zwettler, Victor Youri Helson, Kevin Etienne Robert Roux, Hideki Konishi

We study a Fermi gas with strong, tunable interactions dispersively coupled to a high-finesse cavity. Upon probing the system along the cavity axis, we observe a strong optomechanical Kerr nonlinearity originating from the density response of the gas to th ...

AMER PHYSICAL SOC2022

Liquid metal infiltration of silicon based alloys into porous carbonaceous materials. Part I: Modelling of channel filling and reaction phase formation

Ludger Weber, Alberto Ortona, Manoj Kondibhau Naikade

A relation for an adequate pore fraction needed to obtain residual Si and C free composites via reactive Si-X alloy infiltration is presented. The volume ratio of SiC and carbonaceous phase, the composition of the infiltrating liquid and the apparent densi ...

ELSEVIER SCI LTD2022

Cavity Quantum Electrodynamics with strongly correlated fermions

Kevin Etienne Robert Roux

This thesis presents the first cavity quantum electrodynamics experiments performed with a degenerate gas of

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Li with strong atom-atom interactions. The first part of this manuscript describes the design and the building of the apparatus that has been e ...

EPFL2022

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Patrick Daniel Barth, Dániel Kéri

Genetically-encoded fluorescent sensors for neuromodulators are increasingly used molecular tools in neuroscience. However, these protein-based biosensors are often limited by the sensitivity of the protein scaffold towards endogenous ligands. Here, we exp ...

2021

Colloidal Nanocrystals as Precursors and Intermediates in Solid State Reactions for Multinary Oxide Nanomaterials

Raffaella Buonsanti, Anna Loiudice, Valeria Mantella

Polyelemental compounds with dimensions in the nanosized regime are desirable in a large variety of applications, yet their synthesis remains a general challenge in chemistry. One of the major bottlenecks to obtaining multinary systems is the complexity of ...

AMER CHEMICAL SOC2021

Learning the electronic density of states in condensed matter

Michele Ceriotti, Andrea Anelli, Chiheb Ben Mahmoud

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

AMER PHYSICAL SOC2020

Light-Induced Renormalization of the Dirac Quasiparticles in the Nodal-Line Semimetal ZrSiSe

Majed Chergui, Marco Grioni, Arnaud Magrez, Helmuth Berger, Philippe Bugnon, Alberto Crepaldi, Luca Moreschini, Simon Karl Moser, Gianmarco Gatti, Serhii Polishchuk

In nodal-line semimetals, linearly dispersing states form Dirac loops in the reciprocal space with a high degree of electron-hole symmetry and a reduced density of states near the Fermi level. The result is reduced electronic screening and enhanced correla ...

2020

Colloidal Synthesis of Cu-M-S (M = V, Cr, Mn) Nanocrystals by Tuning the Copper Precursor Reactivity

Raffaella Buonsanti, Seyedehbehnaz Varandili, James Richard Pankhurst, Valeria Mantella

New ternary and higher order inorganic materials are needed for a large variety of applications, yet their synthesis still represents a chemistry challenge. Herein, we focus on the synthesis of Cu-M-S nanocrystals (where M = V, Cr, Mn) via a wet-chemistry ...

AMER CHEMICAL SOC2020

Unsupervised Representation Learning for Gaze Estimation

Jean-Marc Odobez, Yu Yu

Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensiv ...

IEEE2020

Ab initio study of charged states of H in amorphous SiO2

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Machine-learning the electronic density of states: electronic properties without quantum mechanics

Optomechanical response of a strongly interacting Fermi gas

Liquid metal infiltration of silicon based alloys into porous carbonaceous materials. Part I: Modelling of channel filling and reaction phase formation

Cavity Quantum Electrodynamics with strongly correlated fermions

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Colloidal Nanocrystals as Precursors and Intermediates in Solid State Reactions for Multinary Oxide Nanomaterials

Learning the electronic density of states in condensed matter

Light-Induced Renormalization of the Dirac Quasiparticles in the Nodal-Line Semimetal ZrSiSe

Colloidal Synthesis of Cu-M-S (M = V, Cr, Mn) Nanocrystals by Tuning the Copper Precursor Reactivity

Unsupervised Representation Learning for Gaze Estimation

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Cavity Quantum Electrodynamics with strongly correlated fermions

Light-Induced Renormalization of the Dirac Quasiparticles in the Nodal-Line Semimetal ZrSiSe

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Learning the electronic density of states in condensed matter

Colloidal Synthesis of Cu-M-S (M = V, Cr, Mn) Nanocrystals by Tuning the Copper Precursor Reactivity

Liquid metal infiltration of silicon based alloys into porous carbonaceous materials. Part I: Modelling of channel filling and reaction phase formation

Optomechanical response of a strongly interacting Fermi gas

Colloidal Nanocrystals as Precursors and Intermediates in Solid State Reactions for Multinary Oxide Nanomaterials

Unsupervised Representation Learning for Gaze Estimation